Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Hazer-Rau, Dilana; Zhang, Lin; Traue, Harald C.

doi:10.5121/ijaia.2018.9403

Cited by 7 publications

(10 citation statements)

References 16 publications

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“…Further, the present inter-individual analysis is conducted using all physiological signals combined. This is based on our previous results, showing that the signals' combination leads to the highest recognition rates for all the different category-classes [28].…”

Section: Inter-individual Emotion Classificationmentioning

confidence: 92%

“…For the intra-individual analysis, all acquired signals are examined both individually as well as in various combinations among each other. While this is similar to the classification analysis conducted in our previous study [28], the inter-individual analysis is conducted using all signals combined together. In both intra-individual and interindividual, and for each of the resulting signal configuration, the selection of the features is optimized and the resulting selected features are used to train and evaluate a classifier.…”

Section: Pre-processing and Feature Analysismentioning

confidence: 99%

“…In Zhang et al, we conducted a preliminary explicit analysis of various approaches and their combinations in order to evaluate their efficiency in the emotion recognition process [34]. In Hazer-Rau et al the performance of those various emotion classification approaches was explicitly further evaluated as intra-individual analysis [28]. For the present intra-individual and inter-individual analysis, we adopt the approaches which best performed in the first study and which were evaluated for their performance in the latter study.…”

Section: Intra-individual Emotion Classificationmentioning

confidence: 99%

“…In the following, we present individual classifications and subgroup analysis for the human emotion recognition from psychophysiological signals. The application of various feature selection techniques, classification algorithms and evaluation methods is thereby based on our previous performance evaluation study conducted on a larger dataset [28]. In Hazer-Rau et al, we analyzed the combination of approaches that are most reliable for best identifying different emotional states using intra-individual classifications.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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2017

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Human action recognition is still a challenging problem and researchers are focusing to investigate this problem using different techniques. We propose a robust approach for human action recognition. This is achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP) and scale invariant feature transform (SIFT). These features are used to train an MLP neural network during the training stage, and the action classes are inferred from the test videos during the testing stage. The proposed features well match the motion of individuals and their consistency, and accuracy is higher using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly used for human action recognition. In addition, we show that our approach outperforms individual features i.e. considering only spatial and only temporal feature.

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Section: Inter-individual Emotion Classificationmentioning

confidence: 92%

Section: Pre-processing and Feature Analysismentioning

confidence: 99%

Section: Intra-individual Emotion Classificationmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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2017

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“…It is used in this study for the later signal processing and analysis including the feature extraction and feature selection as well as the emotion classification and the evaluation analysis. For the application of these pre-processing steps and for the optimization of the quality of the signals, we adopte our previously developed automation scripts and filtering techniques, composed and implemented as Matlab-based functions [28].…”

Section: Pre-processing and Feature Analysismentioning

confidence: 99%

Individual Emotion Recognition and Subgroup Analysis From Psychophysiological Signals

Zhang¹,

Traue²,

Hazer-Rau³

2018

SIPIJ

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This study involves intra-and inter-individual emotion classifications from psychophysiological signals and subgroup analysis on the influence of gender and age and their interaction on the emotion recognition. Individual classifications are conducted using a selection of feature optimization, classification and evaluation approaches. The subgroup analysis is based on the inter-individual classification. Emotion elicitation is conducted using standardized pictures in the Valence-Arousal-Dominance dimensions and affective states are classified into five different category classes. Advantageous intra-individual rates are obtained via multi-channel classification and the respiration best contributes to the recognition. High interindividual variances are obtained showing large variability in physiological responses between the subjects. Classification rates are significantly higher for women than for men for the 3-category-class of Valence. Compared to old subjects, young subjects have significantly higher rates for the 3-category-class and 2-category-class of Dominance. Moreover, young men's classification performed the best among the other subgroups for the 5-category-class of Valence/Arousal.

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Healthcare-assisted technologies: recent advances, requirements and energy challenges

Hazer-Rau

2021

Nanomaterials and Energy

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Ambient-assisted technologies offer valuable support for persons with special needs, such as older, ill or disabled persons. Relevant support includes assisting in daily life activities, monitoring health conditions, analyzing behavior or affective states and communicating information with caregivers. From a healthcare perspective, these developments are highly valuable and offer new possibilities in the areas of early prediction of diseases and prevention of associated risks. Smart healthcare applications in this context focus on daily activity recognition to provide assistive services for an independent life; on movement tracking and fall detection to prevent accidents and enable interventions; and on physiological health monitoring to assess the vital signals of ill patients and persons at risk. The aim of this paper is to provide an overview on recent activities and advances related to smart healthcare technologies in the context of ambient-assisted living. The paper presents recent studies related to physiological measurements and describes the main requirements and tools needed to realize such systems, including system design, smart sensors, process automation and trust and acceptance aspects. Finally, the paper discusses associated energy challenges and limitations for integrating smart healthcare systems in ambient-assisted environments.

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Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Cited by 7 publications

References 16 publications

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Individual Emotion Recognition and Subgroup Analysis From Psychophysiological Signals

Healthcare-assisted technologies: recent advances, requirements and energy challenges

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